This document describes the key features of Azure ML Experimentation which allows users to conduct machine learning experiments by running distributed TensorFlow or CNTK training jobs, perform hyperparameter searches, capture run metrics and models, and compare runs through leaderboards. It also enables using popular IDEs, editors, notebooks and frameworks while running experiments on the cloud.
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
Azure and/or AWS: How to Choose the best cloud platform for your projectEastBanc Tachnologies
Published on October 10, 2016
Author: Natalia Tsymbalenko www.eastbanctech.com
In today’s cloud era, DevOps, Software Architects, and IT managers either move towards the cloud or consider optimizing their existing cloud solutions. Meanwhile, the cloud provider market is heating up. As all clouds are not created equal, it’s becoming increasingly challenging to choose the best provider for a particular project. So how do you choose the best cloud for your needs?
This workshop helps you choose the best cloud platform for your project. As a platform-agnostic company, we share with you:
• How we evaluate cloud providers for our customers,
• Cloud provider comparisons and results case studies.
From Idea to Model: Productionizing Data Pipelines with Apache AirflowDatabricks
When supporting a data science team, data engineers are tasked with building a platform that keeps a wide range of stakeholders happy. Data scientists want rapid iteration, infrastructure engineers want monitoring and security controls, and product owners want their solutions deployed in time for quarterly reports.
Azure and/or AWS: How to Choose the best cloud platform for your projectEastBanc Tachnologies
Published on October 10, 2016
Author: Natalia Tsymbalenko www.eastbanctech.com
In today’s cloud era, DevOps, Software Architects, and IT managers either move towards the cloud or consider optimizing their existing cloud solutions. Meanwhile, the cloud provider market is heating up. As all clouds are not created equal, it’s becoming increasingly challenging to choose the best provider for a particular project. So how do you choose the best cloud for your needs?
This workshop helps you choose the best cloud platform for your project. As a platform-agnostic company, we share with you:
• How we evaluate cloud providers for our customers,
• Cloud provider comparisons and results case studies.
Scaling ML-Based Threat Detection For Production Cyber AttacksDatabricks
Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.
During this presentation, after walking through a few ways to use MLflow on Azure directly, we'll cover how upcoming solutions from our group leverage MLflow for core functionality. BenchML is a new repository that aims to provide consumers of prebuilt ML endpoints visibility into the performance of each public offering for a given dataset as well as comparing results across multiple offerings. Using MLflow, BenchML is able to remain cloud-agnostic and offer a delightful local experience while leveraging the aforementioned integration to provide Azure users with a fully managed experience.
Speaker Bio: Akshaya is an engineer in the AI Platform at Microsoft, having released both GA versions of Azure Machine Learning over the years and the OSS repo MMLSpark. As the recent version of Azure ML pivoted to become more of an open platform rather than a managed product, his focus has shifted outward for open-source platform definitions for cloud-scale implementations and focused on MLflow for the Azure ML managed tracking store.
This talk was presented at the Bay Area MLflow Meetup at Databricks HQs in San Francisco: https://www.meetup.com/Bay-Area-MLflow/events/266614106/
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...Databricks
Because MLflow is an API-first platform, there are many patterns for using it in complex workflows and integrating it with existing tools. In this talk, we’ll demo a few best practices for using MLflow in a more complex workflow. These include:
* Run multi-step workflows on MLflow, such as data preparation steps followed by training, and organizing your projects so you can automatically reuse past work.
* Tune Hyperparameter on MLflow with open source hyperparameter tuning packages.
* Save a model in MLflow (eg, from a new machine learning library) and deploying it to the existing deployment tools.
Cortana Analytics Workshop: Using the Cortana Analytics ProcessMSAdvAnalytics
Sachin Chouksey, Wee Hyong Tok. In this talk, we will describe a cloud-centric data science process to go from raw data on premise or in the cloud to operationalized machine learning models using only a browser and elastic resources in Azure using various tools like SQL Server on Azure, Azure VMs, Azure Machine Learning, and Azure HDInsight (Hadoop). Go to https://channel9.msdn.com/ to find the recording of this session.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
A fun learning game covering the basics of some common AWS services such as EC2, Cloudwatch, and Amazon S3. Inspired by the popular TV gameshow Catchphrase.
Michael Pearce, DevOps Engineer @ Peak AI.
Disclaimer: Due to the fast moving nature of AWS, this may be out of date!
Adobe decided to use Amazon Web Services as their Platform of Choice. This presentation discusses the details. Mitch Nelson, Director of Managed Services presented at the AWS Enterprise Tour - SF - 2010
A Microservices Framework for Real-Time Model Scoring Using Structured Stream...Databricks
Open-source technologies allow developers to build microservices framework to build myriad real-time applications. One such application is building the real-time model scoring. In this session,
we will showcase how to architect a microservice framework, in particular how to use it to build a low-latency, real-time model scoring system. At the core of the architecture lies Apache Spark’s Structured
Streaming capability to deliver low-latency predictions coupled with Docker and Flask as additional open source tools for model service. In this session, you will walk away with:
* Knowledge of enterprise-grade model as a service
* Streaming architecture design principles enabling real-time machine learning
* Key concepts and building blocks for real-time model scoring
* Real-time and production use cases across industries, such as IIOT, predictive maintenance, fraud detection, sepsis etc.
Batchly is completely built on Amazon Web Services architecture and provides direct support and integration with S3, SQS, RDS and Lambda services. We are also working on building Docker (https://www.docker.com/) support to enable multi cloud environment support and did we mention about in-built support for AWS SPOT instances that can bring down processing costs by as much as 90%!
We all know how to create ML models, but the path to turning them into a highly scalable easy to use system by users is not always clear. What happens when you need to run thousands of them, on many different datasets, simultaneously and at a huge scale? AND, do it reliably so you can sleep well at night!!
To achieve exactly that, we’ve decided to go down the serverless route and build an anomaly detection system on top of it. We’ll go over the pros and cons of building such a system using serverless and when such an approach could work for you.
Our SpotLight anomaly detection system is capable of easily reusing ML models, and scale to run millions of time series simultaneously with ease. Our system eliminates manual work and allows our end users with no scientific background to set anomalies to detect in a plug and play way and get alerts in no time.
In this talk, we’ll walk you through the architecture and share useful ideas you can adopt and implement in your own projects.
Batchly - Cloud scale job scheduler that abstracts AWS cloud management complexities and enables businesses to easily run their data processing/ batch jobs for significant time and cost savings.
Azure Batch - Azure Batch makes it easy to run large-scale parallel and HPC workloads in Azure. Use Batch to scale-out parallel workloads, manage the execution of tasks in a queue, and enable applications to offload compute jobs in the cloud.
Bridging the Completeness of Big Data on DatabricksDatabricks
Data completeness is key for building any machine learning and deep learning model. The reality is that outliers and nulls widely exist in the data. The traditional methods of using fixed values or statistical metrics (min, max and mean) does not consider the relationship and patterns within the data. Most time it offers poor accuracy and would introduce additional outliers. Also, given our large data size, the computation is an extremely time-consuming process and a lot of time it could be constrained by the limited resource on local computer.
To address those issues, we have developed a new approach that will first leverage the similarity within our data points based on the nature of data source then using a collaborative AI model to fill null values and correct outliers.
In this talk, we will walk through the way we use a distributed framework to partition data by KDB tree for neighbor discovery and a collaborative filtering AI technology to fill the missing values and correct outliers. In addition, we will demonstrate how we reply on delta lake and MLflow for data and model management.
Scaling ML-Based Threat Detection For Production Cyber AttacksDatabricks
Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.
During this presentation, after walking through a few ways to use MLflow on Azure directly, we'll cover how upcoming solutions from our group leverage MLflow for core functionality. BenchML is a new repository that aims to provide consumers of prebuilt ML endpoints visibility into the performance of each public offering for a given dataset as well as comparing results across multiple offerings. Using MLflow, BenchML is able to remain cloud-agnostic and offer a delightful local experience while leveraging the aforementioned integration to provide Azure users with a fully managed experience.
Speaker Bio: Akshaya is an engineer in the AI Platform at Microsoft, having released both GA versions of Azure Machine Learning over the years and the OSS repo MMLSpark. As the recent version of Azure ML pivoted to become more of an open platform rather than a managed product, his focus has shifted outward for open-source platform definitions for cloud-scale implementations and focused on MLflow for the Azure ML managed tracking store.
This talk was presented at the Bay Area MLflow Meetup at Databricks HQs in San Francisco: https://www.meetup.com/Bay-Area-MLflow/events/266614106/
Advanced MLflow: Multi-Step Workflows, Hyperparameter Tuning and Integrating ...Databricks
Because MLflow is an API-first platform, there are many patterns for using it in complex workflows and integrating it with existing tools. In this talk, we’ll demo a few best practices for using MLflow in a more complex workflow. These include:
* Run multi-step workflows on MLflow, such as data preparation steps followed by training, and organizing your projects so you can automatically reuse past work.
* Tune Hyperparameter on MLflow with open source hyperparameter tuning packages.
* Save a model in MLflow (eg, from a new machine learning library) and deploying it to the existing deployment tools.
Cortana Analytics Workshop: Using the Cortana Analytics ProcessMSAdvAnalytics
Sachin Chouksey, Wee Hyong Tok. In this talk, we will describe a cloud-centric data science process to go from raw data on premise or in the cloud to operationalized machine learning models using only a browser and elastic resources in Azure using various tools like SQL Server on Azure, Azure VMs, Azure Machine Learning, and Azure HDInsight (Hadoop). Go to https://channel9.msdn.com/ to find the recording of this session.
Big Data Advanced Analytics on Microsoft Azure 201904Mark Tabladillo
This talk summarizes key points for big data advanced analytics on Microsoft Azure. First, there is a review of the major technologies. Second, there is a series of technology demos (focusing on VMs, Databricks and Azure ML Service). Third, there is some advice on using the Team Data Science Process to help plan projects. The deck has web resources recommended. This presentation was delivered at the Global Azure Bootcamp 2019, Atlanta GA location (Alpharetta Avalon).
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
A fun learning game covering the basics of some common AWS services such as EC2, Cloudwatch, and Amazon S3. Inspired by the popular TV gameshow Catchphrase.
Michael Pearce, DevOps Engineer @ Peak AI.
Disclaimer: Due to the fast moving nature of AWS, this may be out of date!
Adobe decided to use Amazon Web Services as their Platform of Choice. This presentation discusses the details. Mitch Nelson, Director of Managed Services presented at the AWS Enterprise Tour - SF - 2010
A Microservices Framework for Real-Time Model Scoring Using Structured Stream...Databricks
Open-source technologies allow developers to build microservices framework to build myriad real-time applications. One such application is building the real-time model scoring. In this session,
we will showcase how to architect a microservice framework, in particular how to use it to build a low-latency, real-time model scoring system. At the core of the architecture lies Apache Spark’s Structured
Streaming capability to deliver low-latency predictions coupled with Docker and Flask as additional open source tools for model service. In this session, you will walk away with:
* Knowledge of enterprise-grade model as a service
* Streaming architecture design principles enabling real-time machine learning
* Key concepts and building blocks for real-time model scoring
* Real-time and production use cases across industries, such as IIOT, predictive maintenance, fraud detection, sepsis etc.
Batchly is completely built on Amazon Web Services architecture and provides direct support and integration with S3, SQS, RDS and Lambda services. We are also working on building Docker (https://www.docker.com/) support to enable multi cloud environment support and did we mention about in-built support for AWS SPOT instances that can bring down processing costs by as much as 90%!
We all know how to create ML models, but the path to turning them into a highly scalable easy to use system by users is not always clear. What happens when you need to run thousands of them, on many different datasets, simultaneously and at a huge scale? AND, do it reliably so you can sleep well at night!!
To achieve exactly that, we’ve decided to go down the serverless route and build an anomaly detection system on top of it. We’ll go over the pros and cons of building such a system using serverless and when such an approach could work for you.
Our SpotLight anomaly detection system is capable of easily reusing ML models, and scale to run millions of time series simultaneously with ease. Our system eliminates manual work and allows our end users with no scientific background to set anomalies to detect in a plug and play way and get alerts in no time.
In this talk, we’ll walk you through the architecture and share useful ideas you can adopt and implement in your own projects.
Batchly - Cloud scale job scheduler that abstracts AWS cloud management complexities and enables businesses to easily run their data processing/ batch jobs for significant time and cost savings.
Azure Batch - Azure Batch makes it easy to run large-scale parallel and HPC workloads in Azure. Use Batch to scale-out parallel workloads, manage the execution of tasks in a queue, and enable applications to offload compute jobs in the cloud.
Bridging the Completeness of Big Data on DatabricksDatabricks
Data completeness is key for building any machine learning and deep learning model. The reality is that outliers and nulls widely exist in the data. The traditional methods of using fixed values or statistical metrics (min, max and mean) does not consider the relationship and patterns within the data. Most time it offers poor accuracy and would introduce additional outliers. Also, given our large data size, the computation is an extremely time-consuming process and a lot of time it could be constrained by the limited resource on local computer.
To address those issues, we have developed a new approach that will first leverage the similarity within our data points based on the nature of data source then using a collaborative AI model to fill null values and correct outliers.
In this talk, we will walk through the way we use a distributed framework to partition data by KDB tree for neighbor discovery and a collaborative filtering AI technology to fill the missing values and correct outliers. In addition, we will demonstrate how we reply on delta lake and MLflow for data and model management.
Visual Studio and Xamarin enable developers to create native Android and iOS apps with world-class tools in a fast, familiar, and flexible way. Join this tour of how you can use your existing C# and .NET skills to create fully native apps on every platform.
Best practices with Microsoft Graph: Making your applications more performant...Microsoft Tech Community
Learn how to take advantage of APIs, platform capabilities and intelligence from Microsoft Graph to make your app more performant, more resilient and more reliable
Build interactive emails for Outlook with Actionable Messages using Adaptive Cards. In this session, you will learn how to code a simple and great looking Actionable Message end-to-end.
As organizations deploy additional security controls to combat today’s evolving threats, integration challenges often limit the return of investment. The new security API in the Microsoft Graph makes it easier for enterprise developers and ISVs to unlock insights from these solutions by unifying and standardizing alerts for easier integration and correlation, bringing together contextual data to inform investigations, and enabling automation for greater SecOps efficiency. We will walk through real world examples of applications that leverage the security API to help customers realize the full value of their security investments.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
Using AML Python SDK
1.
2.
3.
4.
5. • Manage training jobs locally,
scaled-up or scaled-out
• Run distributed TensorFlow or
CNTK training jobs
• Conduct a hyperparameter
search on traditional ML or DNN
• Service side capture of run
metrics, output logs and models
• Leaderboards, side by side run
comparison and model selection
• Use your favorite IDEs, editors,
notebooks, and frameworks
Azure ML Experimentation
U S E T H E M O S T P O P U L A R I N N O V A T I O N S
U S E A N Y T O O L
U S E A N Y F R A M E W O R K O R L I B R A R Y
